IBM AI Meetup Talk by Max Kelsen on business use cases and best practices leveraging natural language processing.

2. Who We Are

3. Natural Language Processing
Nicholas Therkelsen-Terry
CEO, Max Kelsen

4. Definitions
Natural Language Processing: The process of taking data
which is in text, and extract meaning from it.
Regina Barzilay, MIT
(https://soundcloud.com/user-684403695/data-science-demystified-ep-2-regina-barzilay)
Big Data: The three Vs:
1. Volume: n = N
2. Variety: Structured and Unstructured
3. Velocity: Real time and batch
Martin Hilber, University of California

5. A History of NLP in Analytics
1. Keyword Maps
2. Keyword Based NLP
3. Natural Language Classifying (NLC)
4. Machine Learning Based NLP
5. What’s Next?

6. A History of NLP in Analytics
1. Keyword Maps
2. Keyword Based NLP
3. Natural Language Classifying (NLC)
4. Machine Learning Based NLP
5. What’s Next?

7. Types of Tasks Performed
● Sentiment Analysis
● Emotion Analysis
● Topic Tagging
● Named Entity Extraction
● Machine Translation
● Answering Questions
● Natural Language Generation
● Natural Language Summarisation

8. Types of Tasks Performed
● Sentiment Analysis
● Emotion Analysis
● Topic Tagging
● Named Entity Extraction
● Machine Translation
● Question Answering
● Natural Language Generation
● Natural Language Summarisation

9. Text Classification

10. Locato
What is it?
In collaboration with Moreton Bay Regional Council (MBRC), Max Kelsen has developed Locato,
a real-time social media monitoring platform to extract data from social networks to assist in
disaster management during times of crisis.

11. Locato
What is it?
In collaboration with Moreton Bay Regional Council (MBRC), Max Kelsen has developed Locato,
a real-time social media monitoring platform to extract data from social networks to assist in
disaster management during times of crisis.
Challenges
With tens of thousands of tweets and posts relating to disaster events, how do you filter out the
noise and highlight actionable information?

12. Locato
What is it?
In collaboration with Moreton Bay Regional Council (MBRC), Max Kelsen has developed Locato,
a real-time social media monitoring platform to extract data from social networks to assist in
disaster management during times of crisis.
Challenges
With tens of thousands of tweets and posts relating to disaster events, how do you filter out the
noise and highlight actionable information?
Solution
Using IBM Watson’s NLC, we trained a model to classify tweets and posts into three overarching
themes: (1) Alerts (2) Platitudes and (3) Requests

13. Watson NLC
Solution
We trained IBM Watson’s Natural Language Classifier (NLC) to recognise and classify social
posts using real data from social media platforms during previous disaster events. Our training
sets used data from the New South Wales bushfires, Cyclone Yasi, the most recent Queensland
floods and the 2014 hostage crisis at the Lindt Café in Sydney.
The classifications:
● Alerts (posts sharing information related to the disaster event)
● Platitudes (posts expressing kindness, sympathy or other care), and
● Requests (posts asking for general information or assistance)

15. Watson NLC
Challenges
● Irony or sarcasm is difficult to classify. Jokes and sarcasm often follow the same
linguistic patterns as regular posts, creating the potential for them to be identified
incorrectly.
● Platitudes are defined primarily by their emotionality. It is possible to express sadness
or comfort in a tweet, alongside a request for information, or an update about fire or flood
damage.
Outcomes
● We’re comfortable with the performance of the classifier and have moved it into
production.

17. Entity Extraction
Samuel Irvine Casey
COO, Max Kelsen

18. Football Fans: A Case Study
● r/soccer - the footballing subreddit.
● Analysing over two million comments from the
2016-17 Season
● Seek to answer the following primary question:
“Which team attracts the most anger online?”
● What factors affect this anger?
○ Specific Incidents or Games
○ Time
○ Managers
○ Players

19. The Training Process
Entity Type
An entity type is how you categorize a real-world
thing. An entity mention is an example of a thing of
that type. For example, the mention "Luis Suarez"
can be annotated as a PERSON_FOOTBALLER
entity type.
Relation
A relation type defines a binary, ordered
relationship between two entities.
E.g. Luis Suarez is employed by FC Barcelona
Mention Type
Qualifies the mention by certain parts of speech
Name - Proper Nouns: Luis Suarez
Nominal - Common Nouns: Footballer
Pronominal - Pronouns: He
Coreference
A coreference chain links together all references
of the same object, place or person
E.g. Luis Suarez is a Footballer, he has been at
Barcelona for nearly 3 years

20. Example:
Football
Andre Marriner is a f***ing muppet
how has he given the red card to
Gibbs instead of
Oxlade-Chamberlain here, still
Arsenal are playing like s*** at the
moment and are now a man down.
If Chelsea score from this penalty
Arsenal are done.

21. Entity Types: Football
PERSON_REFEREE PERSON_FOOTBALLER KEY_EVENT TEAM

22. Name Nominal Pronominal None
Mention Types: Football

23. Relation Types: Football

24. Preliminary
Results

26. Potential Hypotheses
1. Popularity on Reddit
2. American Bias
3. Historic Rivalries
4. Sense of Entitlement

27. Preventable? The last couple [of goals]
were straight up embarassing, what the
**** even was that.
Rarely do I give the slightest **** about
the reputation of ''English football'' but ****
me, that was tragic.
A lot of people blame Wenger for
Arsenal's woes but the performances
from the players are a ****ing disgrace.
"Shocking! Arsenal surprisingly
collapses and probably crashes out of
the title race, didn't see that coming."
- No one ever, 2017
This is the worst I've seen Arsenal
play since... last week. ****ing hell
that was horrible.
I don't want to hear one more ****ING word about how this team is
"mentally strong." They've just put up two of the most mentally
weak performances I've seen in a while.
That second half was just same old
Arsenal in a nutshell. They concede in
the first 5 minutes and then just play
shellshocked the rest of the game.
They lost every single ball and didn't put
City's defense under ANY pressure.
****ing disgusting this **** and I'm sick of
it.
[Arsenal] are a
steaming pile of horse
****, top to bottom.
Utterly embarassing
What a ****ing toothless, spineless
piece of **** performance.

28. Preventable? The last couple [of goals]
were straight up embarassing, what the
**** even was that.
Rarely do I give the slightest **** about
the reputation of ''English football'' but ****
me, that was tragic.
A lot of people blame Wenger for
Arsenal's woes but the performances
from the players are a ****ing disgrace.
"Shocking! Arsenal surprisingly
collapses and probably crashes out of
the title race, didn't see that coming."
- No one ever, 2017
This is the worst I've seen Arsenal
play since... last week. ****ing hell
that was horrible.
I don't want to hear one more ****ING word about how this team is
"mentally strong." They've just put up two of the most mentally
weak performances I've seen in a while.
That second half was just same old
Arsenal in a nutshell. They concede in
the first 5 minutes and then just play
shellshocked the rest of the game.
They lost every single ball and didn't put
City's defense under ANY pressure.
****ing disgusting this **** and I'm sick of
it.
[Arsenal] are a
steaming pile of horse
****, top to bottom.
Utterly embarassing
What a ****ing toothless, spineless
piece of **** performance.

29. Commercial Applications
Nicholas Therkelsen-Terry
CEO, Max Kelsen

30. Customer Experience Intelligence
cognitiveCX is your Customer Sentiment Nervous System

31. The Old Paradigm
Large organisations spend millions of dollars collecting and analysing new
quantitative data, but often ignore the vast sources of data they already
have available.
80% of contact centres don’t believe their platforms are ready for the future.

32. The Answer is in Your Dark Data
Customers are already telling you about their experience; they call, email and
write to you. They’re talking on social media and in the comments on news sites.
The answer to improving the customer experience you provide is already in your
hands, it’s just a case of unlocking it.

33. Every Organisation is Full of Dark Data

34. Teaching a Machine to Understand
Your Language
Your department, like every business, has a language of its own. The
AI needs to understand this language so that it knows what to focus
on and how to interpret it.
IBM Watson and cognitiveCX makes this possible.

35. In traditional CX data systems, complex customer
interactions are often excessively oversimplified.
For many systems, an interaction like this would be
categorised as:
“negative customer experience, noise complaint”

36. cognitiveCX leverages the learning and
cognitive power of IBM Watson to
thoroughly analyse natural language.
This allows cognitiveCX to determine
emotion, sentiment and relationships
on an entity-by-entity basis.

37. Analysing and enriching data at this granular level allows for thorough,
detailed reporting and insights through cognitiveCX, so that you can
easily find the needles in your CX haystack.
These enrichments and data points allow cognitiveCX to clearly report
all of the facts at hand:
In this example, the customer had a negative, anger-provoking
experience relating to their core issue, but had a positive
experience with the customer support staff at the call center.
cognitiveCX provides you with a clearer and fuller picture of what your
existing customer data really means.

39. Sentiment

40. Emotion

41. Channel

42. Intent

43. Service Area

44. Case Study

45. Case Study – Government
The Client
A Queensland based government body with an extensive range of services and
responsibilities, and a multi-billion dollar budget.
The Challenge
Our client wanted to harness the value within their unstructured, customer interaction data
to identify pain-points in the customer journey.

46. Case Study – Government
Some Stats:
● 25 individual unstructured data sets (including call logs, letters, social media posts
and comments, surveys and more!)
● 1.5 million unstructured data points per year
● 50 different service areas
● 4,000 customer touch points
● 1 AI model

47. Case Study – Solution
What we did
We used the cognitiveCX system, which involved the training and deployment of a
specialised machine learning model. The model was trained to identify and understand key
business units, stakeholders and issues, using the client’s existing data. In a short period,
we identified key customer pain-points through sentiment and emotional analysis.

48. Case Study – Insights
Insight #1
The client’s existing hypothesis was that they were receiving a high number of angry
complaints from cyclists about a lack of cycling infrastructure. They were surprised to find
that in many CX interactions, anger was actually being directed at cyclists by pedestrians
who had been involved in collisions on the footpath or who had become frustrated with the
behaviour of cyclists on shared pedestrian/cyclist paths.

49. Case Study – Challenging Assumptions
Insight #2
We found that there were two key personas when dealing with council: those who transacted
with government and those who acted as good samaritans. The AI identified that out of
these two groups those transacting were well-serviced most of the time, but for those acting
as good samaritans the same processes made the experience cumbersome and frustrating.

50. Case Study – Leveraging cognitiveCX Findings
Outcomes
Our client has created three specialised teams to engage with key problematic CX areas
that were identified with cognitiveCX.
Once these teams are fully implemented, cognitiveCX can monitor their ongoing
effectiveness in real-time.

51. How can cognitiveCX help you deliver
better services to your citizens?
● Monitor all of the interactions your department has with citizens in real-time
● Understand what makes your citizens happy, sad, or angry when they’re
dealing with your organisation
● Identify areas of product, process, or people that need attention
● Inform the design of new citizen experiences

52. Q&A